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Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3526–3550 of 6748 papers

TitleStatusHype
Conformal prediction interval for dynamic time-seriesCode2
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations—0
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time SeriesCode0
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning—0
Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modelingCode0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Neural Ordinary Differential Equations for Intervention ModelingCode1
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
Differentiable Divergences Between Time SeriesCode1
Short-term Wind Speed Forecasting based on LSSVM Optimized by Elitist QPSO—0
An Improved Online Penalty Parameter Selection Procedure for _1-Penalized Autoregressive with Exogenous Variables—0
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
VEST: Automatic Feature Engineering for ForecastingCode1
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—0
Graph Deep Factors for Forecasting—0
Probabilistic Time Series Forecasting with Structured Shape and Temporal DiversityCode1
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings—0
Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data—0
Unfolding recurrence by Green's functions for optimized reservoir computing—0
Stochastic embeddings of dynamical phenomena through variational autoencodersCode0
Modeling Atmospheric Data and Identifying Dynamics: Temporal Data-Driven Modeling of Air Pollutants—0
A Deep Learning Forecaster with Exogenous Variables for Day-Ahead Locational Marginal Price—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified